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Refined Stratified Sampling for efficient Monte Carlo based uncertainty quantification · Study

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ArticleRefined Stratified Sampling for efficient Monte Carlo based uncertainty quantification

Michael D. Shields2015

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Articlegreen2015

Refined Stratified Sampling for efficient Monte Carlo based uncertainty quantification

Michael D. Shields · Kirubel Teferra · Adam Hapij · Raymond P. Daddazio

122 citations·0/0 files stored·Reliability Engineering & System Safety

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Probabilistic and Robust Engineering Design Optimal Experimental Design Methods Mathematical Approximation and Integration

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References

2000A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code 2007Global Sensitivity Analysis. The Primer 2003Monte Carlo Methods in Financial Engineering 1979Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code 2003Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems 1987Large Sample Properties of Simulations Using Latin Hypercube Sampling 1982A distribution-free approach to inducing rank correlation among input variables 1990Minimax and maximin distance designs

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2019A novel fractional moments-based maximum entropy method for high-dimensional reliability analysis 2019Review of statistical model calibration and validation—from the perspective of uncertainty structures 2020UQpy: A general purpose Python package and development environment for uncertainty quantification 2020An adaptive polynomial chaos expansion for high-dimensional reliability analysis 2021Variance-based adaptive sequential sampling for Polynomial Chaos Expansion

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Details

Year2015

Typearticle

Published2015-06-13

VenueReliability Engineering & System Safety

ISSN-L0951-8320

Languageen

Citations122

Files0 public / 0

ShelfProbabilistic and Robust Engineering Design

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doi10.1016/j.ress.2015.05.023

openalex_workW1600223249

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2018Simulation-Informed Probabilistic Methodology for Common Cause Failure Analysis

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2022First-passage probability estimation of high-dimensional nonlinear stochastic dynamic systems by a fractional moments-based mixture distribution approach